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We are happy to share with you the latest developments at Astro Data Lab in this September 2022 newsletter.

In this Newsletter:

  • Invitation to take short survey on behalf of US-ELTP
  • New Datasets at Data Lab for September 2022
  • New Science Example Jupyter Notebooks
  • TIMESTEP Summer Tech Internship
  • Invitation to take short survey on behalf of US-ELTP

    The US Extremely Large Telescope Program (US-ELTP) is a joint endeavor of NSF's NOIRLab and the organizations building the Giant Magellan Telescope (GMT) and the Thirty Meter Telescope (TMT). It will provide all US astronomers the opportunity to use this powerful, bi-hemispheric ELT system and to conduct research using archived data from GMT and TMT. US-ELTP is currently developing their plans for data services and science platforms. Current users of existing science platforms such as NOIRLab's Astro Data Lab and Rubin Science Platform already have significant experience and are invited to take a short survey (5-10 minutes) to help US-ELTP in understanding the community’s needs from a science platform, including desired user support and training. We appreciate you taking the time to fill out the survey which can be found at:


    New Datasets at Data Lab for September 2022

    Astro Data Lab has recently incorporated several new datasets for use by the community: Gaia DR3, SDSS DR17, the Gemini Near-Infrared Spectrograph - Distant Quasar Survey (GNIRS-DQS), and six SDSS DR16 Value-Added Catalogs (VACs).

    Recently added catalogs at Astro Data Lab
    Number of objects
    Survey area (deg2)
    Wavelength coverage (µm)
    Gaia DR3 1.8B All-sky 0.33 - 1.05
    SDSS DR17 5.1M 993 BOSS: 0.36 - 1.04
    SDSS: 0.38 - 0.92
    GNIRS-DQS 260 n/a 1.0 - 2.5

    SDSS DR17 and Gaia DR3 will replace SDSS DR16 and Gaia EDR3 as our spectroscopic and astrometric reference datasets for crossmatching. That means every dataset at Data Lab that has an object table will be crossmatched against SDSS DR17 (for spectroscopy) and Gaia DR3 (for astrometry), and vice versa.. We have also added a few other useful columns such as nest4096, ring256, and htm9 for Healpix-based and Hierarchical Triangular Mesh (HTM)-based sky tessellation use cases. These pre-crossmatched tables are accessible in the schema browser, and through standard TAP/SQL/ADQL queries like all other catalogs at Data Lab.

    The Astro Data Lab team evaluates periodically which external survey datasets we should source, ingest, and serve. We appreciate requests and suggestions from our users. Please contact us at datalab@noirlab.edu to send your request and, if possible, mention an example scientific use case.

    Gaia DR3

    The Gaia DR3 catalog builds upon the Early Data Release 3 (released on 3 December 2020) and combines, for the same stretch of time and the same set of observations, these already-published data products with numerous new data products such as extended objects and non-single stars. There are four Gaia DR3 tables available on Data Lab (descriptions from the Gaia website):

  • gaia_source: This table has an entry for every Gaia observed source as published with this data release. It contains the basic source parameters, in their final state as processed by the Gaia Data Processing and Analysis Consortium from the raw data coming from the spacecraft. The table is complemented with others containing information specific to certain kinds of objects (e.g. Solar–system objects, non–single stars, variables etc.) and value–added processing (e.g. astrophysical parameters etc.).
  • astrophysical_parameters: This is the main table containing the 1D astrophysical parameters produced by the Apsis processing chain developed in Gaia DPAC CU8.
  • galaxy_candidates: This table contains parameters derived from various modules dedicated to the classification and characterisation of sources considered as galaxy candidates. This table has been constructed with the intention to be complete rather than pure and, as such, it will contain a large fraction of non-genuine extragalactic sources.
  • qso_candidates: This table contains parameters derived from various modules dedicated to the classification and characterisation of sources considered as QSO candidates. Together with those, the QSOs used to define the Gaia-CRF3 are also listed in this table. This table has been constructed with the intention to be complete rather than pure and, as such, it will contain a large fraction of non-genuine extragalactic sources.
  • SDSS DR17

    SDSS DR17 is the final release of the SDSS-IV survey. We have loaded the core spectroscopic data tables from SDSS DR17:

  • sdss_dr17.specobjall: Information on all SDSS optical spectra, from the original SDSS-I/II spectra to the completion of the eBOSS survey in SDSS-IV.
  • sdss_dr17.specobj: A subset of specobjall that only includes one spectrum (the best spectrum as determined by SDSS) per object.
  • sdss_dr17.photoplate: Photometric details on the objects that appear in specobjall.

  • We currently do not plan to load any of the Value-Added Catalogs (VACs) released in DR17 so far. VACs for DR17 may be added upon request from the scientific community. Please contact us if you would like to make a DR17 VAC request.

    SDSS DR16 VACs

    The latest additions to SDSS DR16 data tables includes the DR16 QSO catalog, DR16Q, which is intended to be the final QSO catalog of the SDSS-IV survey. The newly added tables are:

  • sdss_dr16.dr16q: All objects from SDSS that were identified as QSOs.
  • sdss_dr16.dr16q_duplicates: Duplicate observations of the same object that were identified as QSOs.
  • sdss_dr16.dr16q_superset: This table includes all objects that were targeted as QSOs, even if they did not turn out to be QSOs after spectra were obtained.
  • sdss_dr16.dr16q_superset_duplicates: Duplicate observations of the same object that were targeted as QSOs, even if they did not turn out to be QSOs after spectra were obtained.
  • sdss_dr16.elg_classifier: A test of classification of Emission Line Galaxies (ELGs).
  • sdss_dr16.spiders_quasar: This VAC is a catalog of multi-wavelength spectral properties for all X-ray selected SPIDERS DR16 AGN. It's based on a clean sample of 2RXS and XMM-Newton quasars.
  • SDSS DR16 Value-Added Catalogs at Astro Data Lab
    Data table
    Number of objects
    Survey area (deg2)
    Wavelength coverage (µm)
    Quasar Superset 1.44M 249 0.36 - 1.0
    Quasar Superset Duplicates 204K - -
    Quasar 750.4K 153 0.36 - 1.0
    Quasar Duplicates 200K - -
    ELG Classifier 49.2K - 0.36 - 1.0
    SPIDERS Quasar 7.67K 1.57 X-ray, H𝛽, MgII, OIII spectral emission lines


    This survey constitutes spectroscopic measurements for 260 sources from the Gemini Near Infrared Spectrograph - Distant Quasar Survey (GNIRS-DQS) as part of a Gemini Observatory Large and Long Program (LLP). Being the largest uniform, homogeneous survey of its kind, it represents a flux-limited sample (≲19.0 mag, ≲16.5 mag) of Sloan Digital Sky Survey (SDSS) quasars at 1.5 < z < 3.5 with a monochromatic luminosity (λLλ) at 5100Å in the range of 10^44-10^46 erg s-1. A combination of the GNIRS and SDSS spectra covers principal quasar diagnostic features in each source: the C IV λ1549, Mg II λ2798, λ2803, Hβ λ4861, and [O III] λ4959, λ5007 emission lines. GNIRS-DQS has four tables (as well as five crossmatch tables):

  • gnirs_dqs.spec_measurements: Main spectral measurements table.
  • gnirs_dqs.spec_measurements_supp: Supplementary spectral measurements table.
  • gnirs_dqs.gaussian_fit_parameters: Independent Gaussian feature fit parameters for each emission line that was fit with both a narrow and broad Gaussian profile. The Gaussian profile peak is based on the peak-fit value.
  • gnirs_dqs.obs_log: Observation log of GNIRS-DQS objects.

  • A Jupyter Notebook explaining how to access the GNIRS-DQS data from Data Lab as well as plotting example spectra data from GNIRS-DQS is available in our notebook suite (see next section).

    New Science Example Jupyter Notebooks

    We have added new science example Jupyter Notebooks to the Data Lab notebook suite:

    1. GOGREEN DR1: Galaxy Cluster Membership

    Author(s): Felix Pat, Stephanie Juneau, and the Astro Data Lab Team

    This notebook aims to visualize sky positions and redshifts of galaxies as a function of their cluster membership. It starts with reading in data tables from the GOGREEN DR1 database and selecting the galaxy cluster with the highest velocity dispersion (a proxy for its dynamical mass). The notebook shows how to retrieve the galaxies around the selected cluster, plot their positions on the sky color-coded by each galaxy’s redshift, and how to separately plot the cluster members and nonmembers. Lastly, the notebook demonstrates how to retrieve an image of the selected cluster (SpARCS1613) and overlay symbols at the position of member and nonmember galaxies (see figure below).

    Figure 1 GOGREEN cluster

    Hubble Space Telescope image of the selected SpARCS1613 cluster retrieved using the SIA service. Some galaxies appear faint, while there are two bright galaxies close to the central region. For galaxies within the image footprint, the overlay circles mark which of them are members (in cyan) or nonmembers (in magenta).

    2. Intro to Spectroscopy from the GOGREEN DR1 Dataset

    Author(s): Felix Pat, Stephanie Juneau, and the Astro Data Lab Team

    This notebook illustrates how to retrieve and display 1D and 2D spectra of a given galaxy, and how to derive the redshift and equivalent width of the [O II]3727 doublet by applying a Gaussian fit with the Astropy library. The results are then used to determine if the galaxy is a member of the cluster, and compared to the results from the GOGREEN team as published in the GOGREEN Data Release 1 paper (Balogh et al. 2021).

    Figure 2 GOGREEN spectrum

    Example one-d and two-d spectra for a selected galaxy in cluster SpARCS1616. We can see some spectral features such as the [OII]3727 emission line (marked with red star in top panel) around 7200 Angstrom, and the [O III]4959,5007 doublet around observed wavelengths 9600-9700 Angstrom.

    3. Exploring Stellar Populations Around the Magellanic Clouds with VHS DR5

    Author(s): Alice Jacques, Robert Nikutta

    In this notebook we aim to reproduce results from El Youssoufi et al. (2021) "Stellar substructures in the periphery of the Magellanic Clouds with the VISTA Hemisphere Survey from the red clump and other tracers". The paper focuses on morphological features in the outskirts of the Magellanic Clouds. Among others, the notebook also reproduces this figure from the paper:

    Figure 3 morphology map

    Morphology maps of young and old stars (Y+O; left) surrounding the MCs, young stars (Y; middle) dominated by the top of the main sequence and supergiant stars, and old stars (O; right) dominated by RGB and RC stars. The three rows show stars selected based on LMC or SMC proper motions (top), LMC proper motions (middle) and SMC proper motions (bottom). The color bars show the number of stars per bin. The central regions of the LMC and SMC have been masked out to emphasize the distribution of stars in the outer regions.

    4. The Gemini Near Infrared Spectrograph - Distant Quasar Survey (GNIRS-DQS) Data Access at Data Lab

    Author(s): Brandon Matthews, Ohad Shemmer, Cooper Dix, and The GNIRS-DQS Collaboration

    The GNIRS-DQS spectral inventory is utilized primarily to develop prescriptions for obtaining more precise redshifts, black hole masses, and accretion rates for quasars. The measurements also further our understanding of the dependence of rest-frame ultraviolet-optical spectral properties of quasars on redshift, luminosity, and Eddington ratio, and test whether the physical properties of the quasar central engine evolve over cosmic time. This notebook shows how to access GNIRS-DQS data at Data Lab, the available tables from GNIRS-DQS, and example plots and spectra using the GNIRS-DQS data, as seen in the figure below.

    Figure 4 gnirs_dqs spectra

    Plotting an example spectrum with Hα emission line fits provided in GNIRS-DQS. This demonstrates how the individual quasar CSV files can be utilized to explore the spectral properties of a given source.

    TIMESTEP Summer Tech Internship

    During Summer 2022, the Astro Data Lab hosted one intern student through the University of Arizona TIMESTEP program (Tucson Initiative for Minoritized student Engagement in Science and TEchnology Program). Undergraduate student Felix Pat worked on two different spectroscopic datasets. Using the GOGREEN DR1 survey, he developed two new Jupyter notebooks, on galaxy cluster membership, and on spectroscopic measurements in GOGREEN. The notebooks are now part of the Astro Data Lab collection available to all users (see article on new notebooks at Data Lab in this newsletter). Felix also worked on applying machine learning algorithms to a sample of 300,000 galaxies and quasars from SDSS DR16. He used autoencoders and other dimensionality reduction methods to reveal trends about various types of galaxies. Felix is currently summarizing his results for a Compendium of Undergraduate Research in Astronomy and Space Science by the Astronomical Society of the Pacific. The Data Lab team wishes Felix success as he continues his studies and career.

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